Valorizing Biowaste for Wastewater Treatment: Dewatering Sludges Using Specified Risk Material-Based Flocculants for Industrial Sustainability
Bibliographic record
Abstract
Abstract Sludges, particularly clay-enriched fluid tailings, are major waste streams disposed from mining and mineral processing industries. To improve the separation of sludges into water and stackable solids, a novel flocculant was developed in this study using peptides from specified risk materials (SRMs), a proteinaceous waste from animal rendering industries; the synthesis was accomplished with polyamidoamine epichlorohydrin (PAE) in a one-pot aqueous reaction. Settling tests using standard kaolinite suspensions showed that compared to a petrochemical-based flocculant (hydrolyzed polyacrylamide, HPAM) widely used in the current mining industry, the SRM-based flocculant achieved a similar settling rate but a more complete ultimate dewatering (sediment volume reduced by 47.5%). Unlike HPAM, the performance of the novel flocculant did not require gypsum, a common industrial processing aid that could be detrimental to downstream processing. Interfacial and particle size analyses revealed that the peptide–PAE materials adsorbed at kaolinite surfaces through electrostatic interactions, reduced the fine solids’ net surface charge (ζ from −40 to −15 mV), and facilitated rapid aggregations of these highly suspended solids. Overall, this proof-of-concept study demonstrates the great potential of using a waste protein-based flocculant to address intractable waste sludge challenges for industrial sustainability as well as reduced environmental footprints.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".